





'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger("""transformers.models.speecht5""")






def   __A    (				a_    :	Optional[int]   ,a_    :	int   ,a_    :	str						):
		hf_model.apply_weight_norm()

		lowerCAmelCase  :     str       						= checkpoint["input_conv.weight_g"]
		lowerCAmelCase  :     List[str]       						= checkpoint["input_conv.weight_v"]
		lowerCAmelCase  :     str       						= checkpoint["input_conv.bias"]

		for i in range(len(config.upsample_rates						)						):
				lowerCAmelCase  :     str       						= checkpoint[f'''upsamples.{i}.1.weight_g''']
				lowerCAmelCase  :     Optional[Any]       						= checkpoint[f'''upsamples.{i}.1.weight_v''']
				lowerCAmelCase  :     List[Any]       						= checkpoint[f'''upsamples.{i}.1.bias''']

		for i in range(len(config.upsample_rates						) * len(config.resblock_kernel_sizes						)						):
				for j in range(len(config.resblock_dilation_sizes						)						):
						lowerCAmelCase  :     Union[str, Any]       						= checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
						lowerCAmelCase  :     List[Any]       						= checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
						lowerCAmelCase  :     int       						= checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

						lowerCAmelCase  :     Optional[int]       						= checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
						lowerCAmelCase  :     List[Any]       						= checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
						lowerCAmelCase  :     Optional[Any]       						= checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

		lowerCAmelCase  :     int       						= checkpoint["output_conv.1.weight_g"]
		lowerCAmelCase  :     Tuple       						= checkpoint["output_conv.1.weight_v"]
		lowerCAmelCase  :     Optional[int]       						= checkpoint["output_conv.1.bias"]

		hf_model.remove_weight_norm()






@torch.no_grad()
def   __A    (				a_    :	Any   ,a_    :	Optional[Any]   ,a_    :	int   ,a_    :	int=None   ,a_    :	Tuple=None   ,):
		if config_path is not None:
				lowerCAmelCase  :     List[Any]       						= SpeechTaHifiGanConfig.from_pretrained(a_						)
		else:
				lowerCAmelCase  :     Optional[Any]       						= SpeechTaHifiGanConfig()

		lowerCAmelCase  :     List[str]       						= SpeechTaHifiGan(a_						)

		lowerCAmelCase  :     List[str]       						= torch.load(a_						)
		load_weights(orig_checkpoint["model"]["generator"]   ,a_   ,a_						)

		lowerCAmelCase  :     Optional[int]       						= np.load(a_						)
		lowerCAmelCase  :     Union[str, Any]       						= stats[0].reshape(-1						)
		lowerCAmelCase  :     Tuple       						= stats[1].reshape(-1						)
		lowerCAmelCase  :     int       						= torch.from_numpy(a_						).float()
		lowerCAmelCase  :     Optional[Any]       						= torch.from_numpy(a_						).float()

		model.save_pretrained(a_						)

		if repo_id:
				print("Pushing to the hub..."						)
				model.push_to_hub(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
					parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)

					lowerCAmelCase									=   parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)










'''simple docstring'''






import math

import tensorflow as tf
from packaging import version






def   __A    (				a_    :	List[Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)   ,x.dtype						)						))

		return x * cdf






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     int       						= tf.cast(math.pi   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(a_   ,3						))						))

		return x * cdf






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)

		return x * tf.tanh(tf.math.softplus(a_						)						)






def   __A    (				a_    :	List[str]						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     Any       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Optional[int]       						= tf.cast(0.7_9_7_8_8_4_5_6_0_8   ,x.dtype						)

		return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Optional[int]       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= tf.cast(1.7_0_2   ,x.dtype						)
		return x * tf.math.sigmoid(coeff * x						)






def   __A    (				a_    :	List[Any]						):
		return tf.clip_by_value(_gelu(a_						)   ,-1_0   ,1_0						)






def   __A    (				a_    :	List[Any]   ,a_    :	List[Any]=-1						):
		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= tf.split(a_   ,2   ,axis=a_						)
		return a * tf.math.sigmoid(a_						)


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):






					def   __A    (				a_    :	Optional[Any]						):
							return tf.keras.activations.gelu(a_   ,approximate=a_						)

					lowerCAmelCase									=   tf.keras.activations.gelu

					lowerCAmelCase									=   approximate_gelu_wrap
else:
					lowerCAmelCase									=   _gelu
					lowerCAmelCase									=   _gelu_new


lowerCAmelCase									=   {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}






def   __A    (				a_    :	int						):
		if activation_string in ACTaFN:
				return ACTaFN[activation_string]
		else:
				raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}'''						)



